|dc.description.abstract||The main objective of the research was to develop a theoretical credit model that incorporates entrepreneurial competencies of farmers as variables in order to determine the repayment ability of the farmer. The research was conducted by using a financial organisation as case to test the application of a statistical credit-scoring model that incorporates entrepreneurial competencies. Entrepreneurial competencies have been found to have an influence on the competitiveness, and, by extension the financial performance of a business. Farms are no different from other businesses, where the aim of the farming business is to ensure profits, and decisions are made accordingly. Individuals that possess higher levels of entrepreneurial competencies are therefore expected to perform better in terms of management and coordination in the business environment, which improves financial performance and repayment ability. The theoretical credit model includes a neural network identified from literature and applied to accurately predict the high-risk loans which are liable to be rejected.
The variables and characteristics used in the credit process were investigated from the credit provider’s viewpoint. Most research on credit tends to report on the variables and characteristics from the borrower’s side, which can result in variables that are important when the lender considers the loan applicant’s ability to repay being omitted. Results indicated that many of the variables used in the decision-making process are based on subjective measures, especially the variables that are associated with managerial and entrepreneurial abilities. The use of human judgement in the credit process is associated with several disadvantages that can influence the decision-making process, specifically consistency in the decision-making. Recommendations are therefore to investigate extending credit models by including entrepreneurial competencies that are measured with the use of an instrument that can provide a consistent reporting method for different applications. Further research is also needed to investigate the implementation of an objective, statistical credit-scoring model in determining the repayment ability of farmers.
The entrepreneurial competencies of the farmers were measured and examined to gain a better understanding and insight into the specific competencies of farmers in South Africa. The entrepreneurial competencies of farmers can be measured with the use of an objective instrument that provides a score for each competency. The entrepreneurial competencies included the following: opportunity; relationship; conceptual; organising; strategic; commitment; learning; and personal strength competencies. Farmers were found to have higher scores in the commitment and relationship competencies, while opportunity competencies had the lowest score for the farmers included in the research. The scores determined for the farmers also provide a consistent measuring instrument that can be used to measure the entrepreneurial and managerial competencies as variables for inclusion in credit-granting decisions.
The entrepreneurial scores were included with other decision-making variables in a statistical credit-scoring model. A back propagation neural network was trained with the use of known input–output combinations, tested and then applied to agricultural credit applications. The entrepreneurial competencies were found to contribute in the decision-making of the network, where the generalised weights compared with age and experience and other scale variables also included in the network. Entrepreneurial competencies can, therefore, also be included in determining the repayment abilities of credit applicants. The use of the studied neural networks in agricultural credit applications require further research, as neural networks are known for exhibiting difficulty in interpreting the results, indicating that providing reasons for a decision can be difficult. The method can, however, be used as a supplementary tool for current methods that may assist in assuring consistency in decision-making, as the neural networks are unable to accommodate additional variables that were not part of the training process.
The main conclusion drawn from the research is that entrepreneurial competencies of farmers can be included with the use of a measuring instrument in a neural network credit model. The model can provide consistency in the decision-making procedure for agricultural loan applications; however, further research is necessary to provide a method that can accommodate the dynamic nature of the agricultural sector where conditions may necessitate the inclusion of additional variables in the decision-making process.||en_ZA